This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

This is a project completed during my internship at Mingxi Capital, where I implemented a data scraping and trading framework based on Binance API. The framework is capable of fetching historical data, real-time data, and executing trading strategies.